Timezone: »
Website: https://sites.google.com/site/kernelgraphical/
Kernel methods and graphical models are two important families of techniques for machine learning. Our community has witnessed many major but separate advances in the theory and applications of both subfields. For kernel methods, the advances include kernels on structured data, Hilbert-space embeddings of distributions, and applications of kernel methods to multiple kernel learning, transfer learning, and multi-task learning. For graphical models, the advances include variational inference, nonparametric Bayes techniques, and applications of graphical models to topic modeling, computational biology and social network problems.
This workshop addresses two main research questions: first, how may kernel methods be used to address difficult learning problems for graphical models, such as inference for multi-modal continuous distributions on many variables, and dealing with non-conjugate priors? And second, how might kernel methods be advanced by bringing in concepts from graphical models, for instance by incorporating sophisticated conditional independence structures, latent variables, and prior information?
Kernel algorithms have traditionally had the advantage of being solved via convex optimization or eigenproblems, and having strong statistical guarantees on convergence. The graphical model literature has focused on modelling complex dependence structures in a flexible way, although approximations may be required to make inference tractable. Can we develop a new set of methods which blend these strengths?
There has recently been a number of publications combining kernel and graphical model techniques, including kernel hidden Markov models [SBSGS08], kernel belief propagation [SGBLG11], kernel Bayes rule [FSG11], kernel topic models [HSHG12], kernel variational inference [GHB12], kernel herding as Bayesian quadrature [HD12], kernel beta processes [RWDC08], a connection between kernel k-means and Bayesian nonparametrics [KJ12] and kernel determinantal point processes for recommendations [KT12]. Each of these results deals with different inference tasks, and makes use of a range of RKHS properties. We propose this workshop so as to "connect the dots" and develop a unified toolkit to address a broad range of learning problems, to the mutual benefit of researchers in kernels and graphical models. The goals of the workshop are thus twofold: first, to provide an accessible review and synthesis of recent results combining graphical models and kernels. Second, to provide a discussion forum for open problems and technical challenges.
Selected bibliography:
%%%%%%%%%%%%%
[SBSGS08] Song, L. and Boots, B. and Siddiqi, S. and Gordon, G. and Smola, A., Hilbert space embeddings of hidden Markov models, ICML'10.
[SGBLG11] Song, L. and Gretton, A. and Bickson, D. and Low, Y. and Guestrin, C., Kernel belief propagation, AISTATS'11.
[FSG11] Fukumizu, K. and Song, L. and Gretton, A., Kernel Bayes rules, NIPS'11.
[HSHG12] Hennig, P. and Stern, D. and Herbrich, R. and Graepel, T., Kernel topic models, AISTATS'12.
[HD12] Huszar, F. and Duvenaud, D., Optimally-weighted herding is Bayesian quadrature, UAI'12.
[RWDC08] Ren, L. and Wang, Y. and Dunson, D.B. and Carin, L., Kernel beta processes, NIPS'11.
[GHB12] Gershman, S. and Hoffman, M. and Blei, D., Nonparametric variational inference, ICML'12.
[KJ12] Kulis, B. and Jordan, M., Revisiting k-means: new algorithms via Bayesian nonparametrics, ICML'12.
[KT12] Kulesza, A. and Taskar, B., Determinantal point processes for machine learning, arXiv:1207.6083
Author Information
Le Song (Ant Financial & Georgia Institute of Technology)
Arthur Gretton (Gatsby Unit, UCL)
Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University. Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).
Alexander Smola (Amazon - We are hiring!)
**AWS Machine Learning**
More from the Same Authors
-
2021 Spotlight: Mixture Proportion Estimation and PU Learning:A Modern Approach »
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2021 : Benchmarking Multimodal AutoML for Tabular Data with Text Fields »
Xingjian Shi · Jonas Mueller · Nick Erickson · Mu Li · Alexander Smola -
2021 : Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning »
Jiani Huang · Ziyang Li · Binghong Chen · Karan Samel · Mayur Naik · Le Song · Xujie Si -
2021 : Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects »
Rahul Singh · Ritsugen Jo · Arthur Gretton -
2021 : Large Scale Coordination Transfer for Cooperative Multi-Agent Reinforcement Learning »
Ethan Wang · Binghong Chen · Le Song -
2021 : Composite Goodness-of-fit Tests with Kernels »
Oscar Key · Tamara Fernandez · Arthur Gretton · Francois-Xavier Briol -
2022 : RLSBench: A Large-Scale Empirical Study of Domain Adaptation Under Relaxed Label Shift »
Saurabh Garg · Nick Erickson · James Sharpnack · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2022 Poster: Adaptive Interest for Emphatic Reinforcement Learning »
Martin Klissarov · Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Taesup Kim · Alexander Smola -
2022 Poster: Optimal Rates for Regularized Conditional Mean Embedding Learning »
Zhu Li · Dimitri Meunier · Mattes Mollenhauer · Arthur Gretton -
2022 Poster: KSD Aggregated Goodness-of-fit Test »
Antonin Schrab · Benjamin Guedj · Arthur Gretton -
2022 Poster: Efficient Aggregated Kernel Tests using Incomplete $U$-statistics »
Antonin Schrab · Ilmun Kim · Benjamin Guedj · Arthur Gretton -
2022 Poster: Faster Deep Reinforcement Learning with Slower Online Network »
Kavosh Asadi · Rasool Fakoor · Omer Gottesman · Taesup Kim · Michael Littman · Alexander Smola -
2022 Poster: Graph Reordering for Cache-Efficient Near Neighbor Search »
Benjamin Coleman · Santiago Segarra · Alexander Smola · Anshumali Shrivastava -
2022 Poster: Uncovering the Structural Fairness in Graph Contrastive Learning »
Ruijia Wang · Xiao Wang · Chuan Shi · Le Song -
2021 Workshop: Machine Learning Meets Econometrics (MLECON) »
David Bruns-Smith · Arthur Gretton · Limor Gultchin · Niki Kilbertus · Krikamol Muandet · Evan Munro · Angela Zhou -
2021 Poster: KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support »
Pierre Glaser · Michael Arbel · Arthur Gretton -
2021 Poster: A Biased Graph Neural Network Sampler with Near-Optimal Regret »
Qingru Zhang · David Wipf · Quan Gan · Le Song -
2021 Poster: Locality Sensitive Teaching »
Zhaozhuo Xu · Beidi Chen · Chaojian Li · Weiyang Liu · Le Song · Yingyan Lin · Anshumali Shrivastava -
2021 Poster: Mixture Proportion Estimation and PU Learning:A Modern Approach »
Saurabh Garg · Yifan Wu · Alexander Smola · Sivaraman Balakrishnan · Zachary Lipton -
2021 Poster: Deep Explicit Duration Switching Models for Time Series »
Abdul Fatir Ansari · Konstantinos Benidis · Richard Kurle · Ali Caner Turkmen · Harold Soh · Alexander Smola · Bernie Wang · Tim Januschowski -
2021 Poster: Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation »
Ritsugen Jo · Heishiro Kanagawa · Arthur Gretton -
2021 Poster: Multi-task Learning of Order-Consistent Causal Graphs »
Xinshi Chen · Haoran Sun · Caleb Ellington · Eric Xing · Le Song -
2021 Poster: RoMA: Robust Model Adaptation for Offline Model-based Optimization »
Sihyun Yu · Sungsoo Ahn · Le Song · Jinwoo Shin -
2021 Poster: Continuous Doubly Constrained Batch Reinforcement Learning »
Rasool Fakoor · Jonas Mueller · Kavosh Asadi · Pratik Chaudhari · Alexander Smola -
2021 Poster: Self-Supervised Learning with Kernel Dependence Maximization »
Yazhe Li · Roman Pogodin · Danica J. Sutherland · Arthur Gretton -
2021 Poster: Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning »
Jiani Huang · Ziyang Li · Binghong Chen · Karan Samel · Mayur Naik · Le Song · Xujie Si -
2020 Poster: Understanding Deep Architecture with Reasoning Layer »
Xinshi Chen · Yufei Zhang · Christoph Reisinger · Le Song -
2020 Poster: A Non-Asymptotic Analysis for Stein Variational Gradient Descent »
Anna Korba · Adil Salim · Michael Arbel · Giulia Luise · Arthur Gretton -
2020 Poster: Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation »
Rasool Fakoor · Jonas Mueller · Nick Erickson · Pratik Chaudhari · Alexander Smola -
2020 Poster: A kernel test for quasi-independence »
Tamara Fernandez · Wenkai Xu · Marc Ditzhaus · Arthur Gretton -
2020 Spotlight: A kernel test for quasi-independence »
Tamara Fernandez · Wenkai Xu · Marc Ditzhaus · Arthur Gretton -
2020 Poster: The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models »
Yingxiang Yang · Negar Kiyavash · Le Song · Niao He -
2020 Spotlight: The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models »
Yingxiang Yang · Negar Kiyavash · Le Song · Niao He -
2019 : Invited Talk - Alexander J. Smola - Sets and symmetries »
Alexander Smola -
2019 Workshop: Learning with Temporal Point Processes »
Manuel Rodriguez · Le Song · Isabel Valera · Yan Liu · Abir De · Hongyuan Zha -
2019 Poster: Neural Similarity Learning »
Weiyang Liu · Zhen Liu · James Rehg · Le Song -
2019 Poster: Meta Architecture Search »
Albert Shaw · Wei Wei · Weiyang Liu · Le Song · Bo Dai -
2019 Poster: Exponential Family Estimation via Adversarial Dynamics Embedding »
Bo Dai · Zhen Liu · Hanjun Dai · Niao He · Arthur Gretton · Le Song · Dale Schuurmans -
2019 Poster: Maximum Mean Discrepancy Gradient Flow »
Michael Arbel · Anna Korba · Adil Salim · Arthur Gretton -
2019 Poster: Retrosynthesis Prediction with Conditional Graph Logic Network »
Hanjun Dai · Chengtao Li · Connor Coley · Bo Dai · Le Song -
2019 Poster: Kernel Instrumental Variable Regression »
Rahul Singh · Maneesh Sahani · Arthur Gretton -
2019 Oral: Kernel Instrumental Variable Regression »
Rahul Singh · Maneesh Sahani · Arthur Gretton -
2019 Tutorial: Interpretable Comparison of Distributions and Models »
Wittawat Jitkrittum · Danica J. Sutherland · Arthur Gretton -
2018 Poster: Informative Features for Model Comparison »
Wittawat Jitkrittum · Heishiro Kanagawa · Patsorn Sangkloy · James Hays · Bernhard Schölkopf · Arthur Gretton -
2018 Poster: Learning Loop Invariants for Program Verification »
Xujie Si · Hanjun Dai · Mukund Raghothaman · Mayur Naik · Le Song -
2018 Spotlight: Learning Loop Invariants for Program Verification »
Xujie Si · Hanjun Dai · Mukund Raghothaman · Mayur Naik · Le Song -
2018 Poster: BRUNO: A Deep Recurrent Model for Exchangeable Data »
Iryna Korshunova · Jonas Degrave · Ferenc Huszar · Yarin Gal · Arthur Gretton · Joni Dambre -
2018 Poster: Coupled Variational Bayes via Optimization Embedding »
Bo Dai · Hanjun Dai · Niao He · Weiyang Liu · Zhen Liu · Jianshu Chen · Lin Xiao · Le Song -
2018 Poster: Learning Temporal Point Processes via Reinforcement Learning »
Shuang Li · Shuai Xiao · Shixiang Zhu · Nan Du · Yao Xie · Le Song -
2018 Spotlight: Learning Temporal Point Processes via Reinforcement Learning »
Shuang Li · Shuai Xiao · Shixiang Zhu · Nan Du · Yao Xie · Le Song -
2018 Poster: Learning towards Minimum Hyperspherical Energy »
Weiyang Liu · Rongmei Lin · Zhen Liu · Lixin Liu · Zhiding Yu · Bo Dai · Le Song -
2018 Poster: On gradient regularizers for MMD GANs »
Michael Arbel · Danica J. Sutherland · Mikołaj Bińkowski · Arthur Gretton -
2017 : Conditional Densities and Efficient Models in Infinite Exponential Families »
Arthur Gretton -
2017 : TBA11 »
Alexander Smola -
2017 : Learning from Conditional Distributions via Dual Embeddings (poster). »
Le Song -
2017 Oral: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Predicting User Activity Level In Point Processes With Mass Transport Equation »
Yichen Wang · Xiaojing Ye · Hongyuan Zha · Le Song -
2017 Poster: Deep Sets »
Manzil Zaheer · Satwik Kottur · Siamak Ravanbakhsh · Barnabas Poczos · Ruslan Salakhutdinov · Alexander Smola -
2017 Poster: Learning Combinatorial Optimization Algorithms over Graphs »
Elias Khalil · Hanjun Dai · Yuyu Zhang · Bistra Dilkina · Le Song -
2017 Spotlight: Learning Combinatorial Optimization Algorithms over Graphs »
Elias Khalil · Hanjun Dai · Yuyu Zhang · Bistra Dilkina · Le Song -
2017 Poster: A Linear-Time Kernel Goodness-of-Fit Test »
Wittawat Jitkrittum · Wenkai Xu · Zoltan Szabo · Kenji Fukumizu · Arthur Gretton -
2017 Poster: Deep Hyperspherical Learning »
Weiyang Liu · Yan-Ming Zhang · Xingguo Li · Zhiding Yu · Bo Dai · Tuo Zhao · Le Song -
2017 Poster: On the Complexity of Learning Neural Networks »
Le Song · Santosh Vempala · John Wilmes · Bo Xie -
2017 Spotlight: Deep Hyperspherical Learning »
Weiyang Liu · Yan-Ming Zhang · Xingguo Li · Zhiding Yu · Bo Dai · Tuo Zhao · Le Song -
2017 Oral: A Linear-Time Kernel Goodness-of-Fit Test »
Wittawat Jitkrittum · Wenkai Xu · Zoltan Szabo · Kenji Fukumizu · Arthur Gretton -
2017 Spotlight: On the Complexity of Learning Neural Networks »
Le Song · Santosh Vempala · John Wilmes · Bo Xie -
2017 Poster: Wasserstein Learning of Deep Generative Point Process Models »
Shuai Xiao · Mehrdad Farajtabar · Xiaojing Ye · Junchi Yan · Xiaokang Yang · Le Song · Hongyuan Zha -
2016 Workshop: Adaptive and Scalable Nonparametric Methods in Machine Learning »
Aaditya Ramdas · Arthur Gretton · Bharath Sriperumbudur · Han Liu · John Lafferty · Samory Kpotufe · Zoltán Szabó -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Learning features to distinguish distributions »
Arthur Gretton -
2016 Oral: Interpretable Distribution Features with Maximum Testing Power »
Wittawat Jitkrittum · Zoltán Szabó · Kacper P Chwialkowski · Arthur Gretton -
2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing -
2016 Poster: Multistage Campaigning in Social Networks »
Mehrdad Farajtabar · Xiaojing Ye · Sahar Harati · Le Song · Hongyuan Zha -
2016 Poster: Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions »
Yichen Wang · Nan Du · Rakshit Trivedi · Le Song -
2016 Poster: Interpretable Distribution Features with Maximum Testing Power »
Wittawat Jitkrittum · Zoltán Szabó · Kacper P Chwialkowski · Arthur Gretton -
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
Sashank J. Reddi · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2015 : Scaling Machine Learning »
Alexander Smola -
2015 : *Arthur Gretton* Learning with Probabilities as Inputs, Using Kernels »
Arthur Gretton -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: Time-Sensitive Recommendation From Recurrent User Activities »
Nan Du · Yichen Wang · Niao He · Jimeng Sun · Le Song -
2015 Poster: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Spotlight: Fast and Guaranteed Tensor Decomposition via Sketching »
Yining Wang · Hsiao-Yu Tung · Alexander Smola · Anima Anandkumar -
2015 Poster: Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families »
Heiko Strathmann · Dino Sejdinovic · Samuel Livingstone · Zoltan Szabo · Arthur Gretton -
2015 Poster: Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients »
Bo Xie · Yingyu Liang · Le Song -
2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2015 Poster: Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression »
Yu-Ying Liu · Shuang Li · Fuxin Li · Le Song · James Rehg -
2015 Poster: COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution »
Mehrdad Farajtabar · Yichen Wang · Manuel Rodriguez · Shuang Li · Hongyuan Zha · Le Song -
2015 Oral: COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution »
Mehrdad Farajtabar · Yichen Wang · Manuel Rodriguez · Shuang Li · Hongyuan Zha · Le Song -
2015 Poster: Fast Two-Sample Testing with Analytic Representations of Probability Measures »
Kacper P Chwialkowski · Aaditya Ramdas · Dino Sejdinovic · Arthur Gretton -
2015 Poster: M-Statistic for Kernel Change-Point Detection »
Shuang Li · Yao Xie · Hanjun Dai · Le Song -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Poster: Communication Efficient Distributed Machine Learning with the Parameter Server »
Mu Li · David G Andersen · Alexander Smola · Kai Yu -
2014 Poster: A Wild Bootstrap for Degenerate Kernel Tests »
Kacper P Chwialkowski · Dino Sejdinovic · Arthur Gretton -
2014 Poster: Active Learning and Best-Response Dynamics »
Maria-Florina F Balcan · Christopher Berlind · Avrim Blum · Emma Cohen · Kaushik Patnaik · Le Song -
2014 Poster: Spectral Methods for Indian Buffet Process Inference »
Hsiao-Yu Tung · Alexander Smola -
2014 Oral: A Wild Bootstrap for Degenerate Kernel Tests »
Kacper P Chwialkowski · Dino Sejdinovic · Arthur Gretton -
2014 Poster: Learning Time-Varying Coverage Functions »
Nan Du · Yingyu Liang · Maria-Florina F Balcan · Le Song -
2014 Poster: Shaping Social Activity by Incentivizing Users »
Mehrdad Farajtabar · Nan Du · Manuel Gomez Rodriguez · Isabel Valera · Hongyuan Zha · Le Song -
2014 Poster: Scalable Kernel Methods via Doubly Stochastic Gradients »
Bo Dai · Bo Xie · Niao He · Yingyu Liang · Anant Raj · Maria-Florina F Balcan · Le Song -
2013 Workshop: New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks »
Urun Dogan · Marius Kloft · Tatiana Tommasi · Francesco Orabona · Massimiliano Pontil · Sinno Jialin Pan · Shai Ben-David · Arthur Gretton · Fei Sha · Marco Signoretto · Rajhans Samdani · Yun-Qian Miao · Mohammad Gheshlaghi azar · Ruth Urner · Christoph Lampert · Jonathan How -
2013 Workshop: Topic Models: Computation, Application, and Evaluation »
David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar -
2013 Workshop: Randomized Methods for Machine Learning »
David Lopez-Paz · Quoc V Le · Alexander Smola -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Poster: B-test: A Non-parametric, Low Variance Kernel Two-sample Test »
Wojciech Zaremba · Arthur Gretton · Matthew B Blaschko -
2013 Poster: A Kernel Test for Three-Variable Interactions »
Dino Sejdinovic · Arthur Gretton · Wicher Bergsma -
2013 Poster: Robust Low Rank Kernel Embeddings of Multivariate Distributions »
Le Song · Bo Dai -
2013 Oral: A Kernel Test for Three-Variable Interactions »
Dino Sejdinovic · Arthur Gretton · Wicher Bergsma -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2013 Poster: Scalable Influence Estimation in Continuous-Time Diffusion Networks »
Nan Du · Le Song · Manuel Gomez Rodriguez · Hongyuan Zha -
2013 Oral: Scalable Influence Estimation in Continuous-Time Diffusion Networks »
Nan Du · Le Song · Manuel Gomez Rodriguez · Hongyuan Zha -
2012 Workshop: Spectral Algorithms for Latent Variable Models »
Ankur P Parikh · Le Song · Eric Xing -
2012 Workshop: Modern Nonparametric Methods in Machine Learning »
Sivaraman Balakrishnan · Arthur Gretton · Mladen Kolar · John Lafferty · Han Liu · Tong Zhang -
2012 Session: Oral Session 10 »
Alexander Smola -
2012 Poster: Learning Networks of Heterogeneous Influence »
Nan Du · Le Song · Alexander Smola · Ming Yuan -
2012 Poster: FastEx: Fast Clustering with Exponential Families »
Amr Ahmed · Sujith Ravi · Shravan M Narayanamurthy · Alexander Smola -
2012 Spotlight: Learning Networks of Heterogeneous Influence »
Nan Du · Le Song · Alexander Smola · Ming Yuan -
2012 Poster: Optimal kernel choice for large-scale two-sample tests »
Arthur Gretton · Bharath Sriperumbudur · Dino Sejdinovic · Heiko Strathmann · Sivaraman Balakrishnan · Massimiliano Pontil · Kenji Fukumizu -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Poster: Kernel Bayes' Rule »
Kenji Fukumizu · Le Song · Arthur Gretton -
2011 Tutorial: Graphical Models for the Internet »
Amr Ahmed · Alexander Smola -
2010 Workshop: Low-rank Methods for Large-scale Machine Learning »
Arthur Gretton · Michael W Mahoney · Mehryar Mohri · Ameet S Talwalkar -
2010 Workshop: Challenges of Data Visualization »
Barbara Hammer · Laurens van der Maaten · Fei Sha · Alexander Smola -
2010 Poster: Word Features for Latent Dirichlet Allocation »
James Petterson · Alexander Smola · Tiberio Caetano · Wray L Buntine · Shravan M Narayanamurthy -
2010 Poster: Optimal Web-Scale Tiering as a Flow Problem »
Gilbert Leung · Novi Quadrianto · Alexander Smola · Kostas Tsioutsiouliklis -
2010 Poster: Multitask Learning without Label Correspondences »
Novi Quadrianto · Alexander Smola · Tiberio Caetano · S.V.N. Vishwanathan · James Petterson -
2010 Poster: Parallelized Stochastic Gradient Descent »
Martin A Zinkevich · Markus Weimer · Alexander Smola · Lihong Li -
2009 Workshop: Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing »
Stephane Canu · Olivier Cappé · Arthur Gretton · Zaid Harchaoui · Alain Rakotomamonjy · Jean-Philippe Vert -
2009 Workshop: Large-Scale Machine Learning: Parallelism and Massive Datasets »
Alexander Gray · Arthur Gretton · Alexander Smola · Joseph E Gonzalez · Carlos Guestrin -
2009 Session: Oral session 10: Neural Modeling and Imaging »
Arthur Gretton -
2009 Poster: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Oral: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Poster: Slow Learners are Fast »
Martin A Zinkevich · Alexander Smola · John Langford -
2009 Poster: Nonlinear directed acyclic structure learning with weakly additive noise models »
Robert E Tillman · Arthur Gretton · Peter Spirtes -
2009 Poster: Distribution Matching for Transduction »
Novi Quadrianto · James Petterson · Alexander Smola -
2009 Poster: A Fast, Consistent Kernel Two-Sample Test »
Arthur Gretton · Kenji Fukumizu · Zaid Harchaoui · Bharath Sriperumbudur -
2009 Spotlight: A Fast, Consistent Kernel Two-Sample Test »
Arthur Gretton · Kenji Fukumizu · Zaid Harchaoui · Bharath Sriperumbudur -
2008 Workshop: Kernel Learning: Automatic Selection of Optimal Kernels »
Corinna Cortes · Arthur Gretton · Gert Lanckriet · Mehryar Mohri · Afshin Rostamizadeh -
2008 Poster: Kernelized Sorting »
Novi Quadrianto · Le Song · Alexander Smola -
2008 Poster: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Poster: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Spotlight: Kernelized Sorting »
Novi Quadrianto · Le Song · Alexander Smola -
2008 Spotlight: Kernel Measures of Independence for non-iid Data »
Xinhua Zhang · Le Song · Arthur Gretton · Alexander Smola -
2008 Oral: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Poster: Tighter Bounds for Structured Estimation »
Olivier Chapelle · Chuong B Do · Quoc V Le · Alexander Smola · Choon Hui Teo -
2008 Session: Oral session 2: Sensorimotor Control »
Arthur Gretton -
2008 Poster: Robust Near-Isometric Matching via Structured Learning of Graphical Models »
Julian J McAuley · Tiberio Caetano · Alexander Smola -
2008 Poster: Learning Taxonomies by Dependence Maximization »
Matthew B Blaschko · Arthur Gretton -
2007 Workshop: Representations and Inference on Probability Distributions »
Kenji Fukumizu · Arthur Gretton · Alexander Smola -
2007 Spotlight: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: Convex Learning with Invariances »
Choon Hui Teo · Amir Globerson · Sam T Roweis · Alexander Smola -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Spotlight: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Poster: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking »
Markus Weimer · Alexandros Karatzoglou · Quoc V Le · Alexander Smola -
2007 Oral: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Poster: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Spotlight: COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking »
Markus Weimer · Alexandros Karatzoglou · Quoc V Le · Alexander Smola -
2007 Demonstration: Elefant »
Kishor Gawande · Alexander Smola · Vishwanathan S V N · Li Cheng · Simon A Guenter -
2007 Spotlight: Convex Learning with Invariances »
Choon Hui Teo · Amir Globerson · Sam T Roweis · Alexander Smola -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola